Fast Weighted Exponential Product Rules for Robust General Multi-Robot Data Fusion

Abstract

This paper considers the distributed data fusion (DDF) problem for general multi-agent robotic sensor networks in applications such as 3D mapping and target search. In particular, this paper focuses on the use of conservative fusion via the weighted exponential product (WEP) rule to combat inconsistencies that arise from double-counting common information between fusion agents. WEP fusion is ideal for fusing arbitrarily distributed estimates in ad-hoc communication network topologies, but current WEP rule variants have limited applicability to general multi-robot DDF. To address these issues, new information-theoretic WEP metrics are presented along with novel optimization algorithms for efficiently performing DDF within a recursive Bayesian estimation framework. While the proposed WEP fusion methods are generalizable to arbitrary probability distribution functions (pdfs), emphasis is placed here on widely-used Bernoulli and Gaussian mixture pdfs. Experimental results for multi-robot 3D mapping and target search applications show the effectiveness of the proposed methods.